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Record W4394680937 · doi:10.5376/ijmz.2024.14.0004

Review of Porcine Disease Resistance Genetic Basis Research Based on GWAS

2024· article· en· W4394680937 on OpenAlexvenueno aff
Xiao Zhu, Xiaofang Lin

Bibliographic record

VenueInternational Journal of Molecular Zoology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsGenome-wide association studyDiseaseResistance (ecology)Basis (linear algebra)Computational biologyBiologyGeneticsMedicineSingle-nucleotide polymorphismMathematicsGeneGenotypePathology

Abstract

fetched live from OpenAlex

This study reviews the latest progress in research on the genetic basis of porcine disease resistance based on GWAS. This study first introduces the importance and challenges of porcine disease resistance to the breeding industry, as well as the history and current status of research on porcine disease resistance; Then elaborates on the role and advantages of GWAS technology in research on the genetic basis of disease resistance, including its characteristics of high-throughput, high-resolution and comprehensive detection of genetic variation; Furthermore, it focuses on the important results achieved by GWAS-based research in discovering genetic variations related to porcine disease resistance, and the impact of these variations on disease resistance. Potential influencing mechanisms of sex. This study also explores the limitations of GWAS in porcine disease resistance research, and looks at the direction and potential contributions of future research, with a view to providing more effective strategies and means for pig health management and the sustainable development of the breeding industry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.626
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.337
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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